Forecasting Practices in US Corporations: Survey Results

Sanders, Nada R.; Manrodt, Karl

doi:10.1287/inte.24.2.92

Cited by 189 publications

(97 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…One study found that 91 percent of more than 60,000 statistical forecasts made in four companies were judgmentally adjusted (Fildes, Goodwin, Lawrence, and Nikolopoulos 2009). Consistent with this finding, a survey of forecasters at 96 U.S. corporations found that about 45 percent of the respondents claimed that they always made judgmental adjustments to statistical forecasts, while only 9 percent said that they never did (Sanders and Manrodt 1994). Legerstee and Franses (2014) found that 99.7 percent of 8,411 one-step-ahead sales forecasts for pharmaceutical products made by 21 experts in 21 countries were adjusted.…”

Section: Avoid Unstructured Judgmental Adjustments To Forecasts (6)mentioning

confidence: 81%

Golden Rule of Forecasting: Be Conservative

2015

View full text Add to dashboard Cite

This paper proposes a unifying theory of forecasting in the form of a Golden Rule of Forecasting. The Golden Rule is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek all knowledge relevant to the problem, and use methods that have been validated for the situation. A checklist of 28 guidelines is provided to implement the Golden Rule. This article's review of research found 150 experimental comparisons; all supported the guidelines. The average error reduction from following a single guideline (compared to common practice) was 28 percent. The Golden Rule Checklist helps forecasters to forecast more accurately, especially when the situation is uncertain and complex, and when bias is likely. Nonexperts who know the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data have led forecasters to violate the Golden Rule. As a result, despite major advances in forecasting methods, evidence that forecasting practice has improved over the past half-century is lacking.

show abstract

Section: Avoid Unstructured Judgmental Adjustments To Forecasts (6)mentioning

confidence: 81%

Golden Rule of Forecasting: Be Conservative

2015

View full text Add to dashboard Cite

show abstract

“…However, such adjustments may be influenced by several biases, including overconfidence in the expert's own judgment (Friedman et al, 2001;Lawrence et al, 2006;Lim & O'Connor, 1996;Sanders, 1997); anchoring and adjustment (i.e., anchoring the forecast to a single cue like the last point or the system forecast, and then making insufficient adjustments to this cue; see Epley & Gilovich, 2006;Fildes et al, 2009;Goodwin, 2005;Lawrence & O'Connor, 1995); and a predisposition to adjust (forecasters making many small harmful adjustments to the system forecasts without any specific reason, leading to a deterioration in accuracy; see Fildes et al, 2009;Lawrence et al, 2006;Önkal, Gönül, & Lawrence, 2008;Sanders & Manrodt, 1994). Usually, large and negative adjustments tend to perform better because they show less bias than positive adjustments (Fildes et al, 2009).…”

Section: Comparison Of Integration Methodsmentioning

confidence: 99%

Expertise, credibility of system forecasts and integration methods in judgmental demand forecasting

Alvarado

Barrero

Önkal

et al. 2017

International Journal of Forecasting

View full text Add to dashboard Cite

a b s t r a c tExpert knowledge elicitation lies at the core of judgmental forecasting-a domain that relies fully on the power of such knowledge and its integration into forecasting. Using experts in a demand forecasting framework, this work aims to compare the accuracy improvements and forecasting performances of three judgmental integration methods. To do this, a field study was conducted with 31 experts from four companies. The methods compared were the judgmental adjustment, the 50-50 combination, and the divide-and-conquer. Forecaster expertise, the credibility of system forecasts and the need to rectify system forecasts were also assessed, and mechanisms for performing this assessment were considered. When (a) a forecaster's relative expertise was high, (b) the relative credibility of the system forecasts was low, and (c) the system forecasts had a strong need of correction, judgmental adjustment improved the accuracy relative to both the other integration methods and the system forecasts. Experts with higher levels of expertise showed higher adjustment frequencies. Our results suggest that judgmental adjustment promises to be valuable in the long term if adequate conditions of forecaster expertise and the credibility of system forecasts are met.

show abstract

“…Kahn (2002) realizou estudos onde sugerem que tomadores de decisão preferem utilizar os métodos qualitativos ao invés dos métodos quantitativos. Os responsáveis pela tomada de decisão não estão familiarizados com métodos quantitativos e a aplicação de métodos qualitativos cria um sentimento de controle e de posse sobre o sistema de previsão (SANDERS; MANRODT, 1994;GOODWIN, 2002). Um dos métodos qualitativos mais utilizados para previsão das vendas é o método de Delphi, apresentado em RITZMAN e KRAJEWSKI (2004).…”

Section: Métodos Qualitativosunclassified